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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

共 7554 条记录，以下是81-90 订阅

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Implementation of Face Feature Algorithms for Authentication of a Person (IFFAP) 3

Implementation of Face Feature Algorithms for Authentication...

引用

3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Ramachandra, A.C. Monisha, B. Muralapur, Poorvika Vasupraneetha, H. Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

The present conventional digital financial transaction at Automated Teller Machines (ATM) is secured with PIN number, with this there are chances of forgetting or misusing to overcome this with the increase in technology and various advancements in financial infrastructure, most of the bank customers prefer to use Automated Teller Machines (ATM). To improve the security in such cases a new generation ATM machine which is based on face recognition technology is used. It also assists with changing passwords which increases privacy and security. The high-quality image has an important role in the recognition process. We propose Implementation of Face Feature Algorithms for Authentication of a Person (IFF AP) technique, an attempt to solve, The LBP algorithm and Haar Cascade techniques are employed for this process. This technology helps in increased security and avoids unauthorized transactions. This mutually benefits banking institutions and the customers. Leveraging existing algorithms, particularly those based on Local Binary pattern (LBP) and Haar Cascade, reveals a commendable qualitative analysis showcasing high accuracy up to 92.85% and improves computational efficiency compared to existing Techniques. © 2024 IEEE.

关键词： Local binary pattern

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Modulation pattern recognition Based on TR-Unet 6

Modulation Pattern Recognition Based on TR-Unet

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6th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Chai, Yanzhe University of Jinan Jinan China

ISBN: (纸本)9798350374315

Under the complex electromagnetic environment, Automatic Modulation recognition (AMR) needs to improve the recognition accuracy. To address such problems, we design a Transformer ResNet-Unet (TR-Unet) model, which combines the Transformer and the Unet model with identity shortcut connections. Identity shortcut connections are from the residual network (ResNet). The original in-phase and quadrature (IQ) signals are input directly into the network. We design multi-layer encoder and decoder to extract features and introduce identity shortcut connection to preserve feature details. We utilize the Transformer instead of the convolution in the Unet model. This is to provide more comprehensive feature information extraction capability for the model and to reduce misjudgment and omission of the modulated signal. The training process is conducted with RML2016.10b, while the testing process is conducted with RML2016.10a. The results of simulation experiments show the classification accuracy of our proposed model can reach 92% when the signal-to-noise Ratio (SNR) is 12dB. This proves the effectiveness and reliability of TR-Unet. © 2024 IEEE.

关键词： Signal modulation

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An Air-Written Real-Time English Isolated Numeral recognition 6th

An Air-Written Real-Time English Isolated Numeral Recognitio...

引用

6th international conference on soft computing and its Engineering Applications, icsoftComp 2024

作者： Patil, Chandrashekhar H. Jabde, Meenal K. Department of Computer Science Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India

ISBN: (纸本)9783031880384

Real-time air-writing numeral datasets are extensively utilized for various applications, including pattern identification of handwritten characters or numerals. The banking industry, reservation counters, online education platforms, operating rooms, and other settings frequently employ air-writing technologies. The English language require more air-written isolated numeric datasets in order to be used for pattern recognition. Thus, for English, the current work presents fresh datasets written in the air. Furthermore, a comprehensive unique technique for air-written real-time English isolated numeral and script categorization recognition is proposed in this paper. We have achieved highest accuracy for English, which is 99.25%. We collected samples from 100 people in age groups spanning from 20 to 40 for each language in order to construct the dataset. Every person wrote the numbers 0 through 9 ten times in the air for each language, producing 10,000 pictures for English language. The suggested dataset is openly accessible and can be a useful tool for study on pattern recognition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Air-written CNN English numeral dataset pattern recognition Real time

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The technique of parsing vehicle wiring harness tooling diagram based on pattern recognition 3

The technique of parsing vehicle wiring harness tooling diag...

引用

3rd international conference on High Performance computing and Communication Engineering, HPCCE 2023

作者： Guo, Zhibo Jiang, Wei Song, Yanhai Sun, Chenglin Wang, Nan Qiao, Xiaoli Changchun University of Science and Technology Changchun China Changchun CRRC Railway Vehicles Co. Ltd. Changchun China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

This paper analyzes the main elements of vehicle wire harness diagrams and summarizes the rules that a correct wire harness diagram should adhere to. Building upon this foundation, with the aid of Teigha, successful extraction of entities within vehicle wire harness diagrams has been achieved. Node recognition Algorithm and Wire harness recognition algorithm have been designed, which can correctly analyze elements in the harness diagram and extract data from them. A comparative experiment was conducted with the traditional electronic wiring board, and the experimental results showed that the intelligent electronic wiring board using the algorithm shortened the average path resolution time from 4.3 hours to 7.6 seconds, and no errors occurred during the processes. © 2024 SPIE.

关键词： Vehicles

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Vehicle Theft Detection Using Neural pattern recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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2023 10th international conference on soft computing and Machine Intelligence, ISCMI 2023

2023 10th International Conference on Soft Computing and Mac...

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10th international conference on soft computing and Machine Intelligence, ISCMI 2023

ISBN: (纸本)9798350359374

The proceedings contain 41 papers. The topics discussed include: optimizing vehicle loan default detection with synthetic balancing techniques;LigMANet: towards designing a lightweight crowd counting model;optimization of acoustic and flow performance in a perforated muffler;complexity measurement for multitask classification problems in machine learning;prototype of a system to assist the urologist in the recognition of urinary bladder tumors in MRI images;sign language translator for dumb and deaf;didactic system for the control of bleeding;classifying DNS over HTTPS malicious/benign traffic using deep learning models;and a study on developmental artificial neural networks that integrate multiple functions using variational autoencoder.

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Complex scene fall detection method based on improved YOLOv5 24

Complex scene fall detection method based on improved YOLOv5

引用

13th international conference on computing and pattern recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. The proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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An Improved soft-YOLOv5 Algorithm Applied to Computer Motherboard Defects Detection 6

An Improved SOFT-YOLOv5 Algorithm Applied to Computer Mother...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Zhang, Zhen Zhou, Yujie Xie, Rule Wang, Zhen Huizhou University Openharmony Innovation Lab Huizhou China

ISBN: (纸本)9798350325485

As far as we know, computers are very mature electronic products. With time passing, most computers are assembled by the manufacturer before distribution. However, a problem occurs when people send their computers for repair. Maintainers often increase the time cost of maintenance due to some subtle components on the computer motherboard. In order to improve the efficiency of maintenance and quickly locate the place that needs to be repaired or replaced, in this paper, we improve the NMS operation in Yolov5 by using the soft-NMS algorithm, which successfully improves the performance of YOLOv5 in defect detection on computer motherboards. Compared to YOLOv5, the precision value of soft-YOLOv5 increases from 83.3% to 84.2% and the mAP@0.5 value of soft-YOLOv5 increases from 87.1% to 90.3%. Consequently, soft-YOLOv5 has better performance on computer motherboard defect detection. © 2023 IEEE.

关键词： Defects

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Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel computing 4

Optimization Study of KNN Classification Algorithm on Large-...

引用

4th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2023

作者： Cao, Kangjie Wu, Jinyao Huang, Qianhang Gan, Yichao Minzu University of China Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350343113

The K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression problems. However, the KNN algorithm faces serious challenges when dealing with high-dimensional large-scale datasets, mainly in terms of high computational complexity, low search efficiency, and difficulty in real time. In this study, an optimization strategy based on balanced KD tree and multithreaded parallel computing is proposed to address this problem. Experimental results show that the optimized KNN algorithm significantly improves the search speed while maintaining classification accuracy, realizes real-time classification of large-scale datasets, and provides a powerful tool for pattern recognition, data mining, and other fields in the era of big data. © 2023 IEEE.

关键词： pattern recognition

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A Hybrid Method of SVG Parameter Calibration Based on Particle Swarm Optimization and soft Actor-Critic 7

A Hybrid Method of SVG Parameter Calibration Based on Partic...

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7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhao, Ao Xiang, Xuanchen Zhao, Ligang Zhen, Hongyue Diao, Ruisheng Yan, Kaibiao Xu, Guangjun ZJU-UIUC Institute Zhejiang University Haining China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350350890

Static var generators (SVGS) are essential for compensating reactive power and maintaining bus voltage levels before and after large disturbances. Accurate modeling of SVG controller parameters is critical to ensure the reliability of dynamic simulation in power system operation. This paper introduces a novel SVG parameter identification method, which integrates the benefits of particle swarm optimization (PSO) and reinforcement learning (RL) to improve the efficiency and accuracy of parameter calibration. We use simulation data generated by PSD BPA software combined with actual power grid data as the environment to optimize the parameter settings of SVG. This study combined PSO with the soft Actor-Critic (SAC) algorithm to optimize and calibrate these parameters. Compared with the simulation results, our method shows significant advantages in calibration accuracy and processing speed and effectively improves the simulation quality and practicability of the SVG model. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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